Explicit and implicit category learning in categorical visual search

Categorical search has been heavily investigated over the past decade, mostly using natural categories that leave the underlying category mental representation unknown. The categorization literature offers several theoretical accounts of category mental representations. One prominent account is that separate learning systems account for classification: an explicit learning system that relies on easily verbalized rules and an implicit learning system that relies on an associatively learned (nonverbalizable) information integration strategy. The current study assessed the contributions of these separate category learning systems in the context of categorical search using simple stimuli. Participants learned to classify sinusoidal grating stimuli according to explicit or implicit categorization strategies, followed by a categorical search task using these same stimulus categories. Computational modeling determined which participants used the appropriate classification strategy during training and search, and eye movements collected during categorical search were assessed. We found that the trained categorization strategies overwhelmingly transferred to the verification (classification response) phase of search. Implicit category learning led to faster search response and shorter target dwell times relative to explicit category learning, consistent with the notion that explicit rule classification relies on a more deliberative response strategy. Participants who transferred the correct category learning strategy to the search guidance phase produced stronger search guidance (defined as the proportion of trials on which the target was the first item fixated) with evidence of greater guidance in implicit-strategy learners. This demonstrates that both implicit and explicit categorization systems contribute to categorical search and produce dissociable patterns of data.